Artificial Intelligence vs Machine Learning vs Deep Learning

The terms “machine learning”, “deep learning” and “artificial intelligence” are buzzwords that everyone seems to be talking about these days. What do these terms mean? They are often used interchangeably, which isn’t right. This article will help you understand what these terms mean and how they are different.

Evolution of Artificial Intelligence

The birth of the term “Artificial Intelligence” can be traced back to 1956 when it was formally coined by John McCarthy. Back then, the term was used to describe machines that would possess the same characteristics of human intelligence. This concept of AI, also known as “General AI”, is still a “concept” and not something we’ve been able to practically pull off — at least not yet.

Today, with AI technologies, we can perform specific tasks or solve certain problems as well as or better than, what we humans can. Recent advancements in algorithm, high-end computing power and storage facilities are enabling the creation of machines that can do more than we’ve ever imagined. The exponential increase in the size of the data that’s generated today is one of the reasons too.

Now that we have more data than our human brains can handle, AI systems are being built that can solve more complex problems and make more accurate predictions. Examples include virtual assistants like Siri and Cortana, voice-based personal assistants like Amazon Echo and Google Home, and many more.

Artificial Intelligence is the Umbrella Term

When we come to understand how AI, machine learning (ML), and deep learning (DL) are related, the easiest way is to visualize them in the form of concentric circles where, AI — the very first concept or idea — is the largest circle; the second circle is machine learning, and finally, the third circle — which fits inside both the circles — is deep learning.

In short, AI is the broader concept of machines mimicking human capabilities, while machine learning is a subset. Now, the question is what led to the creation of machine learning?

In order for AI systems to improve into more robust versions, researchers started exploring if these systems could learn from data and get better with experience. Thus, machine learning was born. These new systems differed from older AI systems in their ability to learn and improve over time when exposed to new data. One of the major application of machine learning has been to improve computer vision, which is the ability of a machine to recognize an object in an image or video.

Machine Learning Vs. Deep Learning

Now let’s try to understand how machine learning differs from deep learning with the help of an example: Identifying the image of a cat.

What are the attributes based on which we may recognize an image of a cat? It has two eyes, two ears, four legs, one tail, and whiskers. But the problem is, a dog also has those exact same features. So, when you ask a system to identify a cat image from thousands of other images, based on these attributes, it may wrongly pick an image of a dog.

What’s the solution? You gather hundreds of thousands of images and then manually tag them. For instance, you tag only those pictures that have a cat in them versus those that do not. Then, the machine can be “trained” through continuous feeding of data (in this case, different images with cats in them). Once the machine demonstrates a high accuracy level in identifying a cat from any given image, we can finally say that the machine has “learned” what a cat looks like.

In contrast to this, deep learning will allow a system to auto-learn the distinguishing features of a cat so that it can correctly identify an image as that of a cat and not a dog.

Machine Learning Mastery defines deep learning “as a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks”. These neural networks have many hidden layers, hence the label “deep”.

Simply put, deep learning pushes the boundaries of machine learning capabilities. While deep learning is still in its early stages, it already demonstrates some compelling use cases. One of the most common examples of deep learning applications that we encounter almost every day is our email’s spam filtering system.

From image recognition and language translation by Google to self-driving vehicles and advanced robotics — deep learning forms the basis of many wonderful applications and innovations. For instance, an algorithm powered by deep learning won a Merck sponsored design challenge to identify high-potential molecules that might help creation of new drugs.

However, this is just the beginning. Further advancements, particularly in natural language understanding, indicate the potential of deep learning to enable an even greater degree of automation. We at Intuceo have automated pre-built machine learning engines which enables rapid experimentation to surface actionable insights and accelerates your AI journey